Precision Health Analytics With Predictive Analytics and Implementation Research

Pearson, Thomas A.; Califf, Robert M.; Roper, Rebecca; Engelgau, Michael M.; Khoury, Muin J.; Alcántara, Carmela; Blakely, Craig; Boyce, Cheryl Anne; Brown, Marishka K.; Croxton, Thomas L.; Fenton, Kathleen N.; Parker, Melissa C. Green; Hamilton, Andrew; Helmchen, Lorens A.; Hsu, Lucy L.; Kent, David M.; Kind, Amy; Kravitz, John; Papanicolaou, George; Prosperi, Mattia; Quinn, Matt; Price, LeShawndra N.; Shireman, Paula K.; Smith, Sharon; Szczesniak, Rhonda; Goff, David C.; Mensah, George A.

doi:10.1016/j.jacc.2020.05.043

Cited by 32 publications

(27 citation statements)

References 37 publications

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“…analytics monitoring, especially in a busy, complex, clinical environment, 9 can prove challenging, especially with bedside clinicians whose hard-earned wisdom may not be easily modified. 10 New strategies are required.…”

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confidence: 99%

Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring

Moorman¹

2021

Appl Clin Inform

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A new development in the practice of medicine is Artificial Intelligence-based predictive analytics that forewarn clinicians of future deterioration of their patients. This proactive opportunity, though, is different from the reactive stance that clinicians traditionally take. Implementing these tools requires new ideas about how to educate clinician users to facilitate trust and adoption and to promote sustained use. Our real-world hospital experience implementing a predictive analytics monitoring system that uses electronic health record and continuous monitoring data has taught us principles that we believe to be applicable to the implementation of other such analytics systems within the health care environment. These principles are mentioned below:• To promote trust, the science must be understandable.• To enhance uptake, the workflow should not be impacted greatly.• To maximize buy-in, engagement at all levels is important.• To ensure relevance, the education must be tailored to the clinical role and hospital culture.• To lead to clinical action, the information must integrate into clinical care.• To promote sustainability, there should be periodic support interactions after formal implementation.

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Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring

Moorman¹

2021

Appl Clin Inform

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“…Such frameworks can serve a foundation for work by researchers and practitioners in the field of precision public health to promote health equity. Finally, a need to build capacity for precision public health research through training the next generation of precision public health researchers has been established in the literature ( Allen et al, 2019a , 2019b ; Pearson et al, 2020 ). This group called for specific attention to promoting diversity among precision public health researchers, aligning with calls for promoting diversity in genomics research more broadly ( Robbins et al, 2021 ).…”

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confidence: 99%

Using a Participatory Approach to Develop Research Priorities for Future Leaders in Cancer-Related Precision Public Health

Roberts

Mader²,

Turbitt

et al. 2022

Front. Genet.

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Precision public health is an emerging discipline combining principles and frameworks of precision health with the goal of improving population health. The development of research priorities drawing on the strengths of precision and public health is critical to facilitate the growth of the discipline to improve health outcomes. We held an interactive workshop during a virtual conference bringing together early-career researchers across public health disciplines to identify research priorities in precision public health. The workshop participants discussed and voted to identify three priority areas for future research and capacity building including 1) enhancing equity and access to precision public health research and resources, 2) improving tools and metrics for evaluation and 3) applying principles of implementation science to support sustainable practices. Participants also developed future objectives for achieving each priority. Future efforts by working groups will continue the process of identifying, revising, and advancing critical research priorities to grow the impact of precision public health.

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“…A major innovation in data-powered health insights is the predictive modeling of specific outcomes, such as blood glucose levels. Although predictive models do not necessarily reveal causes and effects, these models have been used for discovery, hypothesis testing, risk prediction, and the identification of counterfactuals and effective interventions [ 43 ]. Despite promising evidence on machine learning models, such as one study that demonstrated a significantly reduced glycemic response when a machine learning model was paired with a dietary intervention, blood glucose forecasts remain a nascent technique.…”

Section: Discussionmentioning

confidence: 99%

Effects of a Novel Blood Glucose Forecasting Feature on Glycemic Management and Logging in Adults With Type 2 Diabetes Using One Drop: Retrospective Cohort Study

Imrisek¹,

Lee²,

Goldner³

et al. 2022

JMIR Diabetes

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Background Personalized feedback is an effective behavior change technique frequently incorporated into mobile health (mHealth) apps. Innovations in data science create opportunities for leveraging the wealth of user data accumulated by mHealth apps to generate personalized health forecasts. One Drop’s digital program is one of the first to implement blood glucose forecasts for people with type 2 diabetes. The impact of these forecasts on behavior and glycemic management has not been evaluated to date. Objective This study sought to evaluate the impact of exposure to blood glucose forecasts on blood glucose logging behavior, average blood glucose, and percentage of glucose points in range. Methods This retrospective cohort study examined people with type 2 diabetes who first began using One Drop to record their blood glucose between 2019 and 2021. Cohorts included those who received blood glucose forecasts and those who did not receive forecasts. The cohorts were compared to evaluate the effect of exposure to blood glucose forecasts on logging activity, average glucose, and percentage of glucose readings in range, after controlling for potential confounding factors. Data were analyzed using analysis of covariance (ANCOVA) and regression analyses. Results Data from a total of 1411 One Drop users with type 2 diabetes and elevated baseline glucose were analyzed. Participants (60.6% male, 795/1311; mean age 50.2 years, SD 11.8) had diabetes for 7.1 years on average (SD 7.9). After controlling for potential confounding factors, blood glucose forecasts were associated with more frequent blood glucose logging (P=.004), lower average blood glucose (P<.001), and a higher percentage of readings in range (P=.03) after 12 weeks. Blood glucose logging partially mediated the relationship between exposure to forecasts and average glucose. Conclusions Individuals who received blood glucose forecasts had significantly lower average glucose, with a greater amount of glucose measurements in a healthy range after 12 weeks compared to those who did not receive forecasts. Glucose logging was identified as a partial mediator of the relationship between forecast exposure and week-12 average glucose, highlighting a potential mechanism through which glucose forecasts exert their effect. When administered as a part of a comprehensive mHealth program, blood glucose forecasts may significantly improve glycemic management among people living with type 2 diabetes.

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Precision Health Analytics With Predictive Analytics and Implementation Research

Cited by 32 publications

References 37 publications

Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring

Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring

Using a Participatory Approach to Develop Research Priorities for Future Leaders in Cancer-Related Precision Public Health

Effects of a Novel Blood Glucose Forecasting Feature on Glycemic Management and Logging in Adults With Type 2 Diabetes Using One Drop: Retrospective Cohort Study

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